Pressure ulcers (PrU) are among the most significant complications in Veterans with spinal cord impairment (SCI) in terms of quality of life and cost of care. The currently used risk assessment tool, the Braden Scale, suffers from a number of limitations. (1) The Braden scale is not SCI-specific and has severe ceiling effects in the SCI population. VA External Peer Review Program (EPRP) surveys, conducted between 2008 and 2010 at 22 VA SCI/D Centers, found on average 95.3% of all patients received a Braden score within 24 hours of an impatient admission with 91.3% of those measured identified as being at high risk. (2) Some risk variables for which there is research evidence or strong clinical support are not well represented in existing assessment tool. (3) The current risk assessment tool was primarily developed for use in the inpatient setting. However, after the acute post-injury period, most individuals with SCI acquire PrUs outside the hospital. Based on the EPRP survey between 2008 and 2010, less than 2% of Veterans admitted to VHA SCI/D Centers in 2010 developed hospital acquired PrUs. The Aims for this study are: 1) Develop natural language processing (NLP) programs to identify the occurrence of PrUs; 2) Develop predictive models of occurrence of PrUs based on available structured data for early impact on PrU risk assessment; 3) Develop NLP programs to reliably extract information about potential predictors from text in clinical notes; 4) Combine risk information obtained through structured and text- extracted NLP data, and develop robust risk assessment predictive of PrUs. Project Methods: This is a retrospective cohort study of Veterans with SCI. The inception of the cohort includes all Veterans with SCI cared for in the VHA in FY 2009 that had no record of a pressure ulcer in the previous 12 months. Potential risk factors (e.g. demographics, diseases status, co-morbidities, health behaviors, psychosocial factors, home care) identified in the literature will be reviewed by an expert panel for logical consistency, completeness and clinical relevance. Review of the EHR will be conducted to determine if the identified risk factors are found in structured (coded in database/table) or in narrative data (text in clinical notes). All structured and narrative data for the targeted cohort or FY 2009-2013 (anticipated most recent data available) will be obtained through the VA Informatics and Computing Infrastructure (VINCI). In Aim 1 we will use 2X2 (Chi-square test) frequency tables to compare the rates of PrU occurrence based on NLP with those based on structured (ICD-9-CM) data. In Aim 2 predictive models of PrU occurrence based on available structured data alone will be developed and compared with the predictions based on the Braden Scale. In Aim 3 NLP systems will be developed to extract risk factors from the EHR text. In Aim 4 predictions based on the new risk models, combining structured data with NLP data will be compared with predictions based on structured data alone and the Braden Scale. Prediction models will be developed with multivariable logistic regression models.